{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Multiclass Classification of White Wine Quality with Support Vector Machine and Feature Union\n",
    "\n",
    "## Wine Data\n",
    "Data from http://archive.ics.uci.edu/ml/datasets/Wine+Quality\n",
    "\n",
    "### Citations\n",
    "P. Cortez, A. Cerdeira, F. Almeida, T. Matos and J. Reis. \n",
    "Modeling wine preferences by data mining from physicochemical properties.\n",
    "In Decision Support Systems, Elsevier, 47(4):547-553. ISSN: 0167-9236.\n",
    "\n",
    "Available at:\n",
    "- [@Elsevier](http://dx.doi.org/10.1016/j.dss.2009.05.016)\n",
    "- [Pre-press (pdf)](http://www3.dsi.uminho.pt/pcortez/winequality09.pdf)\n",
    "- [bib](http://www3.dsi.uminho.pt/pcortez/dss09.bib)\n",
    "\n",
    "Dua, D. and Karra Taniskidou, E. (2017). UCI Machine Learning Repository [http://archive.ics.uci.edu/ml]. Irvine, CA: University of California, School of Information and Computer Science.\n",
    "## Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "white_wine = pd.read_csv('../../ch_10/data/winequality-white.csv', sep=';')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## EDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fixed acidity</th>\n",
       "      <th>volatile acidity</th>\n",
       "      <th>citric acid</th>\n",
       "      <th>residual sugar</th>\n",
       "      <th>chlorides</th>\n",
       "      <th>free sulfur dioxide</th>\n",
       "      <th>total sulfur dioxide</th>\n",
       "      <th>density</th>\n",
       "      <th>pH</th>\n",
       "      <th>sulphates</th>\n",
       "      <th>alcohol</th>\n",
       "      <th>quality</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7.0</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.36</td>\n",
       "      <td>20.7</td>\n",
       "      <td>0.045</td>\n",
       "      <td>45.0</td>\n",
       "      <td>170.0</td>\n",
       "      <td>1.0010</td>\n",
       "      <td>3.00</td>\n",
       "      <td>0.45</td>\n",
       "      <td>8.8</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6.3</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.34</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.049</td>\n",
       "      <td>14.0</td>\n",
       "      <td>132.0</td>\n",
       "      <td>0.9940</td>\n",
       "      <td>3.30</td>\n",
       "      <td>0.49</td>\n",
       "      <td>9.5</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8.1</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.40</td>\n",
       "      <td>6.9</td>\n",
       "      <td>0.050</td>\n",
       "      <td>30.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>0.9951</td>\n",
       "      <td>3.26</td>\n",
       "      <td>0.44</td>\n",
       "      <td>10.1</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7.2</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.32</td>\n",
       "      <td>8.5</td>\n",
       "      <td>0.058</td>\n",
       "      <td>47.0</td>\n",
       "      <td>186.0</td>\n",
       "      <td>0.9956</td>\n",
       "      <td>3.19</td>\n",
       "      <td>0.40</td>\n",
       "      <td>9.9</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7.2</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.32</td>\n",
       "      <td>8.5</td>\n",
       "      <td>0.058</td>\n",
       "      <td>47.0</td>\n",
       "      <td>186.0</td>\n",
       "      <td>0.9956</td>\n",
       "      <td>3.19</td>\n",
       "      <td>0.40</td>\n",
       "      <td>9.9</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   fixed acidity  volatile acidity  citric acid  residual sugar  chlorides  \\\n",
       "0            7.0              0.27         0.36            20.7      0.045   \n",
       "1            6.3              0.30         0.34             1.6      0.049   \n",
       "2            8.1              0.28         0.40             6.9      0.050   \n",
       "3            7.2              0.23         0.32             8.5      0.058   \n",
       "4            7.2              0.23         0.32             8.5      0.058   \n",
       "\n",
       "   free sulfur dioxide  total sulfur dioxide  density    pH  sulphates  \\\n",
       "0                 45.0                 170.0   1.0010  3.00       0.45   \n",
       "1                 14.0                 132.0   0.9940  3.30       0.49   \n",
       "2                 30.0                  97.0   0.9951  3.26       0.44   \n",
       "3                 47.0                 186.0   0.9956  3.19       0.40   \n",
       "4                 47.0                 186.0   0.9956  3.19       0.40   \n",
       "\n",
       "   alcohol  quality  \n",
       "0      8.8        6  \n",
       "1      9.5        6  \n",
       "2     10.1        6  \n",
       "3      9.9        6  \n",
       "4      9.9        6  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "white_wine.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fixed acidity</th>\n",
       "      <th>volatile acidity</th>\n",
       "      <th>citric acid</th>\n",
       "      <th>residual sugar</th>\n",
       "      <th>chlorides</th>\n",
       "      <th>free sulfur dioxide</th>\n",
       "      <th>total sulfur dioxide</th>\n",
       "      <th>density</th>\n",
       "      <th>pH</th>\n",
       "      <th>sulphates</th>\n",
       "      <th>alcohol</th>\n",
       "      <th>quality</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>4898.000000</td>\n",
       "      <td>4898.000000</td>\n",
       "      <td>4898.000000</td>\n",
       "      <td>4898.000000</td>\n",
       "      <td>4898.000000</td>\n",
       "      <td>4898.000000</td>\n",
       "      <td>4898.000000</td>\n",
       "      <td>4898.000000</td>\n",
       "      <td>4898.000000</td>\n",
       "      <td>4898.000000</td>\n",
       "      <td>4898.000000</td>\n",
       "      <td>4898.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>6.854788</td>\n",
       "      <td>0.278241</td>\n",
       "      <td>0.334192</td>\n",
       "      <td>6.391415</td>\n",
       "      <td>0.045772</td>\n",
       "      <td>35.308085</td>\n",
       "      <td>138.360657</td>\n",
       "      <td>0.994027</td>\n",
       "      <td>3.188267</td>\n",
       "      <td>0.489847</td>\n",
       "      <td>10.514267</td>\n",
       "      <td>5.877909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.843868</td>\n",
       "      <td>0.100795</td>\n",
       "      <td>0.121020</td>\n",
       "      <td>5.072058</td>\n",
       "      <td>0.021848</td>\n",
       "      <td>17.007137</td>\n",
       "      <td>42.498065</td>\n",
       "      <td>0.002991</td>\n",
       "      <td>0.151001</td>\n",
       "      <td>0.114126</td>\n",
       "      <td>1.230621</td>\n",
       "      <td>0.885639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>3.800000</td>\n",
       "      <td>0.080000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>0.009000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>0.987110</td>\n",
       "      <td>2.720000</td>\n",
       "      <td>0.220000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>6.300000</td>\n",
       "      <td>0.210000</td>\n",
       "      <td>0.270000</td>\n",
       "      <td>1.700000</td>\n",
       "      <td>0.036000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>108.000000</td>\n",
       "      <td>0.991723</td>\n",
       "      <td>3.090000</td>\n",
       "      <td>0.410000</td>\n",
       "      <td>9.500000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>6.800000</td>\n",
       "      <td>0.260000</td>\n",
       "      <td>0.320000</td>\n",
       "      <td>5.200000</td>\n",
       "      <td>0.043000</td>\n",
       "      <td>34.000000</td>\n",
       "      <td>134.000000</td>\n",
       "      <td>0.993740</td>\n",
       "      <td>3.180000</td>\n",
       "      <td>0.470000</td>\n",
       "      <td>10.400000</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>7.300000</td>\n",
       "      <td>0.320000</td>\n",
       "      <td>0.390000</td>\n",
       "      <td>9.900000</td>\n",
       "      <td>0.050000</td>\n",
       "      <td>46.000000</td>\n",
       "      <td>167.000000</td>\n",
       "      <td>0.996100</td>\n",
       "      <td>3.280000</td>\n",
       "      <td>0.550000</td>\n",
       "      <td>11.400000</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>14.200000</td>\n",
       "      <td>1.100000</td>\n",
       "      <td>1.660000</td>\n",
       "      <td>65.800000</td>\n",
       "      <td>0.346000</td>\n",
       "      <td>289.000000</td>\n",
       "      <td>440.000000</td>\n",
       "      <td>1.038980</td>\n",
       "      <td>3.820000</td>\n",
       "      <td>1.080000</td>\n",
       "      <td>14.200000</td>\n",
       "      <td>9.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       fixed acidity  volatile acidity  citric acid  residual sugar  \\\n",
       "count    4898.000000       4898.000000  4898.000000     4898.000000   \n",
       "mean        6.854788          0.278241     0.334192        6.391415   \n",
       "std         0.843868          0.100795     0.121020        5.072058   \n",
       "min         3.800000          0.080000     0.000000        0.600000   \n",
       "25%         6.300000          0.210000     0.270000        1.700000   \n",
       "50%         6.800000          0.260000     0.320000        5.200000   \n",
       "75%         7.300000          0.320000     0.390000        9.900000   \n",
       "max        14.200000          1.100000     1.660000       65.800000   \n",
       "\n",
       "         chlorides  free sulfur dioxide  total sulfur dioxide      density  \\\n",
       "count  4898.000000          4898.000000           4898.000000  4898.000000   \n",
       "mean      0.045772            35.308085            138.360657     0.994027   \n",
       "std       0.021848            17.007137             42.498065     0.002991   \n",
       "min       0.009000             2.000000              9.000000     0.987110   \n",
       "25%       0.036000            23.000000            108.000000     0.991723   \n",
       "50%       0.043000            34.000000            134.000000     0.993740   \n",
       "75%       0.050000            46.000000            167.000000     0.996100   \n",
       "max       0.346000           289.000000            440.000000     1.038980   \n",
       "\n",
       "                pH    sulphates      alcohol      quality  \n",
       "count  4898.000000  4898.000000  4898.000000  4898.000000  \n",
       "mean      3.188267     0.489847    10.514267     5.877909  \n",
       "std       0.151001     0.114126     1.230621     0.885639  \n",
       "min       2.720000     0.220000     8.000000     3.000000  \n",
       "25%       3.090000     0.410000     9.500000     5.000000  \n",
       "50%       3.180000     0.470000    10.400000     6.000000  \n",
       "75%       3.280000     0.550000    11.400000     6.000000  \n",
       "max       3.820000     1.080000    14.200000     9.000000  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "white_wine.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 4898 entries, 0 to 4897\n",
      "Data columns (total 12 columns):\n",
      "fixed acidity           4898 non-null float64\n",
      "volatile acidity        4898 non-null float64\n",
      "citric acid             4898 non-null float64\n",
      "residual sugar          4898 non-null float64\n",
      "chlorides               4898 non-null float64\n",
      "free sulfur dioxide     4898 non-null float64\n",
      "total sulfur dioxide    4898 non-null float64\n",
      "density                 4898 non-null float64\n",
      "pH                      4898 non-null float64\n",
      "sulphates               4898 non-null float64\n",
      "alcohol                 4898 non-null float64\n",
      "quality                 4898 non-null int64\n",
      "dtypes: float64(11), int64(1)\n",
      "memory usage: 459.2 KB\n"
     ]
    }
   ],
   "source": [
    "white_wine.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'quality score')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = white_wine.quality.value_counts().sort_index(\n",
    "    ascending=False\n",
    ").plot.barh(title='White Wine Quality Scores', figsize=(12, 3))\n",
    "for bar in ax.patches:\n",
    "    ax.text(\n",
    "        bar.get_width(), \n",
    "        bar.get_y() + bar.get_height()/4, \n",
    "        f'{bar.get_width()/white_wine.shape[0]:.1%}'\n",
    "    )\n",
    "plt.xlabel('count of wines')\n",
    "plt.ylabel('quality score')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## White wine quality multi-class classification\n",
    "\n",
    "### Train test split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "y = white_wine.quality\n",
    "X = white_wine.drop(columns=['quality'])\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    X, y, test_size=0.15, random_state=0, stratify=y\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Building model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture --no-stdout\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.feature_selection import SelectFpr\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.pipeline import FeatureUnion, Pipeline\n",
    "from sklearn.preprocessing import PolynomialFeatures, StandardScaler\n",
    "from sklearn.svm import SVC\n",
    "\n",
    "pipeline = Pipeline([\n",
    "    ('scale', StandardScaler()), \n",
    "    ('feature_union', FeatureUnion([\n",
    "        ('interaction_terms', PolynomialFeatures(interaction_only=True)), \n",
    "        ('fpr', SelectFpr())\n",
    "    ])),\n",
    "    ('svm', SVC(gamma='auto', random_state=0, probability=True))\n",
    "])\n",
    "\n",
    "search_space = {\n",
    "    'feature_union__interaction_terms__include_bias' : [True, False],\n",
    "    'svm__C' : [0.5, 1, 5]\n",
    "}\n",
    "\n",
    "grid = GridSearchCV(pipeline, search_space, scoring='f1_macro', cv=5).fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'feature_union__interaction_terms__include_bias': False, 'svm__C': 5}"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid.best_params_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Evaluating the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "preds = grid.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           3       0.00      0.00      0.00         3\n",
      "           4       0.62      0.21      0.31        24\n",
      "           5       0.65      0.61      0.63       219\n",
      "           6       0.57      0.76      0.65       330\n",
      "           7       0.56      0.29      0.38       132\n",
      "           8       0.60      0.23      0.33        26\n",
      "           9       0.00      0.00      0.00         1\n",
      "\n",
      "   micro avg       0.59      0.59      0.59       735\n",
      "   macro avg       0.43      0.30      0.33       735\n",
      "weighted avg       0.59      0.59      0.57       735\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\molinstefanie\\packt\\venv\\lib\\site-packages\\sklearn\\metrics\\classification.py:1143: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "c:\\users\\molinstefanie\\packt\\venv\\lib\\site-packages\\sklearn\\metrics\\classification.py:1143: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "c:\\users\\molinstefanie\\packt\\venv\\lib\\site-packages\\sklearn\\metrics\\classification.py:1143: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "print(classification_report(y_test, preds))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x127937f0>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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MkvTv3ZMaNWqSnJxMcnIyU16dHtV4wRZ8Op8H7h9LQX4Bgwafw5Arh1aK2MHiqQYXToK7u7wnd1e1345z7S5PVbuW91ylSQ4kc+PIWzm0w2Hs3LmDC88dzDHdunHgge28DrWPX1f8zFtvvs4LU6YRqFKF64ZfyfEnnkTLVq09i7Fg5Z/M/WUTVxyTVrTvveUbmbEsE4BT2jfkjI4pvLRwHcszd7Jk7QoAWtStxrDj07h99i+elQXg9geepnbdekXPR9w2rujxy+Mfo0bNWp7GK82zEydTv359X2IVys/PZ9zYe3n2uedJTU3lwvPO5uQePTmwXfR/12IZu7g4ym9lN1FV9RPgR6C2uy1394Wrh6p2iUZyA2jcOIVDOxwGQM2atWjT9kA2ZmZGI9Q+Vv72Gx0P70y16tUJBAIcceRRzPvof57G+HljNjtz8v+yb3deQdHjqoEkCpfL3hO0/4BAkq+LK6sqX83/H8edfJp/QX2WvmwpaWmtaJGWRpWqVendtx/zPp6b8LGLS0qSsLdYC+cu6rnAgzgLNwvwhIiMVNU3oly2iK1bm8FPy5fT8fDOvsQ7sF17nv7vY2zZ8ifVDqjGgs/mc2iHjr7EPqtTKt3a1CM7p4AHP/6taP8RzeswuHMqtQ8I8J/5v3saUwTuv20EIkLPvoPo2XdQ0Ws/pX9L3foNaNI8+lMwCcLwq4YgAoPPOY+zzj4v6jEBsjIzadJ07+XolNRUli1dmvCxi0u0JurtwFGqmgUgIo2B/wHhJDgFPhARBZ5V1fHFDxCRocBQgCeeeobLryjfdYXs7J3cfMO13HzraGrV8qeZ1KbtgVxy2RWM+L8hVK9Rg/YHHUJycrIvsd9clsmbyzLpe2hjerZvyNvpWQAsXruNxWu3cVDjGgzqlMpD81Z6FvOuRyZQv2Fjtm7ZzAOjr6FZWisO6XQEAF/M+4DjTj7ds1ihTJr8Co1TUtm8aRNXX3U5rVu35YiuR0U9rrJvldivP/ZYxq4occsjnLuoSYXJzbUpzPcBHK+qRwB9gOEi0r34Aao6XlW7qmrX8ia33Nxcbr7+Wvr0O4Nep/rbRDpz0Nm89OqbjJ80hbp16tKyZStf43/1+xaObFF3n/0/b8ymca2q1KrqXcKt37AxAHXrNeDIbifz608/AJCfn8c3C+ZxTPdTPIsVSuOUVAAaNGxIj56nkJ7uT00mNbUJG9ZvKHqelZlJSkpKwscuLp7uooaTqOaIyPsi8g8R+QcwC5gdzslVdZ37MwuYARxd3oKGiME9d91Bm7YH8vdLL/P69GXavHkTABvWr+Pjjz7ktD79oh4zpVbVosddmtdhw/Y9++xvWb8agSRhR7Hrd+W1e/cudmXvLHqcvvgrWrQ+EID0b7+hWVorGjZO9SRWKLuys9m5c0fR4y+/WEC7dgdFPS7AYR07sXr1KjIy1pCbk8Oc2bM4qUfPhI9dnIiEvcVamU1UVR0pIoOB43GuwY1X1RllvU9EauLU/ra7j08D7t3fAhe35NvFzHrnbdq3P4jzBg8E4JrrbuDE7id5HapEt950Hdu2biE5EGDk6DupU2ff2tT+uOq4NA5OqUmtAwI8NOAQ3k7PpFPT2jSpfQCKsmlnLpMXrgXgyBZ16NamPvkFSk6+8sznqz0rx7Y/N/PYvSMB545etx6n07nrcQB8Oe8D324ubNq8iZuvv6aoHL379KfbCSf6EjsQCDD69rsYNvQKCgryGThoMO3atU/42MVVgLwVNonWAAURaYtTawMnkb6iqmNDvSc7NzajJXLzYjdI44a3v49ZbFuTofKpFmC/P3yvJ74I+w9m7ojjYvpll1qDE5HPVPUEEdkOf7nCKYCqasjfUFX9DfDndqYxxjdJcVSFKzXBqeoJ7s/a/hXHGFPRxVF+K/smgzubSJn7jDGVg5c3GURkkohkiUh60L67RWStiCxxt75Br40WkRUi8pOIlNkvKZx+cIcVK1AAODKM9xljEpDHAxReAP7LvtOvPaqqDwXvEJEOwPk4OakZ8D8ROUhVS+0qUGoNzs2U24HDRWSbu20HMoG3y/VRjDFxz8uhWqo6H9gcZugzgVdVdY+qrgRWUEbXs1ITnKr+y73+9qCq1nG32qraUFVHh1kgY0yCkUj+ExkqIguDtnB7818jIkvdJmzhrArNgTVBx2S4+0oVTkffr0WkqHOXiNQTkYFhFtIYk2CSJPwteKSSu+0zXLMET+PMIt4FWA887O4vqUoYsstKOAlujKpuLTqb6hZgTBjvM8YkoGiPZFDVTFXNV9UC4Dn2NkMzgLSgQ1sA60KdK6yxqCXss9W4jKmkoj0WVUSaBj0dhDOrOMBM4HwROUBE2gDtga9DnSucRLVQRB4BnsSpDo4AFkVcamNMQvCyo6+ITAVOBhqJSAZO6/BkEemCk29WAVcBqOr3IjIN+AHIA4aHuoMK4SW4EcCdwGs4beAPgOHl+TBlkf0fRVIuSkHZB0VJn0Maxix297Nuj1nsl16IXexe7WMzCwdAdQ9nd4kVLyeyVNULStg9McTxY4GQQz6DhTPYficwKtwTGmMSWzyNZAg1FvUxVb1eRN6hhDsVFWXRGWOMvxJiLCpQOBzroRDHGGMqmfhJb6EH2y9yf0aywIwxJsFVhIkswxWqibqMEJ3oVPXwqJTIGFOhVYDFssIWqona3/1ZeMe0sMl6EeDdasLGmLhSEZYDDFeoJurvACJyvKoeH/TSKBFZQBSmHzfGVHzx1EQNZyRDTRE5ofCJiHQDakavSMaYiiySsaixFk5H3yHAJHfAvQJbgcujWipjTIUVTzW4cDr6LgI6i0gdnEVqtpb1HmNM4oqf9BbelOWpIjIReE1Vt4pIBxEZ4kPZjDEVUHKShL3FWjhN1BeA54HCwYM/44xLLXW8mJ/G3DGa+fPn0aBBQ6a/9W7U49035nYWzP+E+g0aMHX6TAC2bt3CHbfcxLp1a2nWrDljH3zE8/VRCy2Y9TqLPpoFCKkt23LWsFuZOeERVv7wHdVqOJdGB189iqat2+1XnBap9Zhw3yWkNqxDgSqTpi/gyanzuP2qvlx+Vjc2/uksvjzmvzN5/7MfaFC3Jq88OIQjD2vFlJlfcsMDr+/vR/2LBe++zsKPZoFAk7S2nHX1rVSpegAA70z6D4s/fo8xL83xNCbAP+/e++/9yhvOv/cTjz7IZ/PnEahShRYt0rjjnrHUrh39ZRAXfDqfB+4fS0F+AYMGn8OQK8OdO9Jb8dREDecmQyNVnQbOiHRVzQPCWi7dnRzzDRH5UUSWi8hx+1HWEg0YeBZPPTPB69OWqv+AQTz21F/n7Js8aQJdjzmW6e/MoesxxzJ5UnTKs23zRr54702G/etZrn34ebQgn2WffwRA74v/j2v+PYFr/j1hv5MbQF5+AaMeeZO/Df4nJ13yEFed151D2jYB4IkpH3Ps+fdz7Pn38/5nPwCwe08u9z71LqMfLXNN8Iht3byRL96bztX3P8t1D79AQUFB0efO+PVHdrsr3UdDvzMG8eiTf/33PvrYbrz8+tu8PO0t0lq15sVJz0UtfqH8/HzGjb2Xp56ZwIyZs5gz+11+XbEi6nFLEu3pkrwUToLbKSINcTv9isixODcawvEfYI6qHoKzRurycpUyhCO7HkWdutGpLZXkb0d23ad2Nn/eR/Q7w5nkuN8ZA/nk47lRi19QkE9uzh7y852ftetHZzaSDX9sY8mPGQDsyN7Djys30KxxvVKPz96dw+dLfmP3ntyolGfv584jN2c3tes3oqAgnzlTnuH0i/8vKjHB/fcu9vt1zHHHEwg4jZ+OnTqTlbkhavELpS9bSlpaK1qkpVGlalV69+3HvCj+noWSJBL2FmvhJLgbcSaaO9Dt/zYZZwqlkNybEt1xm7KqmuPOBpxwNm/aRKPGjQFo1Lgxf24Odw2NyNRp0JgT+p/LQ1efxwNXDeaA6jVp3/koAP736kSeGDmE2S8+SV5ujqdxWzZtQJeDW/BN+ioA/u/87nz92mieGXMR9WpX9zRWSeo2aMwJZ5zHg8PO5f6hg6lWoxbtOx/Fl3NmcOiRx1MnSkk+HO+8/SbHHX9i1ONkZWbSpGmToucpqalkZmZGPW5JEqYGJyJJQDXgJKAbzsRzh6nq0jDO3RbYCDwvIt+KyAQR2af/XPCiFBMnhDNde+W1a8d2li/8nJv+O5Vbn3mD3D27WfLph5x2wZVc9+iLDBv3NNk7tjH/7amexaxZvSpTH7qCkQ9NZ/vO3Tz3+qd0OONujjn/fjb8sY37bzzLs1il2bVjO8u/WcDNT77KqGenk7N7F99+8j7pX8zj2D6Doh6/NM9PeIZAcjK9+54R9VhawqjJWF0Li/aU5V4KmeDcOdEfVtU8Vf1eVdNVNdw2SAA4AnhaVf8GlDivXPCiFEOuiM1F0/3VoGFD/ti4EYA/Nm6kfoMGUYnz67JF1E9pQs069UgOBOhw9Ims/imd2vUbIiIEqlTliJP7sPbXHz2JFwgkMfWhK3ntvYW8/dF3AGRt3k5BgaKqTHpzAV07tvIkVigrli2ifkrTos992DHdmTvteTZtWMsj117Eg8PPIzdnDw+PuDDqZSk0a+ZbLJj/CfeM/bcvf8ipqU3YsH5vUzgrM5OUlNhM3JksEvYWa+E0UT8QkcES+b9iBpChql+5z9/ASXgJ58STejDrnbcAmPXOW3Q/uWdU4tRtlELGLz+Qs2c3qsqv6Ytp3LwV2//cBICqsvybz0hJa+NJvGfGXMRPKzfw+JSPivY1abT3buGZPTvzw6/rPYkVSr1GKawJ/tzLFnN8/3MZ/dwMRj75GiOffI0qVQ/gpideiXpZAL5Y8CkvvTCBBx97kmrVo99EBzisYydWr15FRsYacnNymDN7Fif1iM7vWVkSbSTDjThDs/JEZDdOPz9V1ZD3xVV1g4isEZGDVfUnoBfOXOqeGjXyRhZ+8zVbtvzJab26M+zqEQwafI7XYYrcMepmFi/8mi1bttD/tB4MHXYNl15+JbfdcgMzZ0ynSdOmjHvw0ajETmvfgcOOOYmnRg0lKSmZpm3ac9Qp/XnxX6PI3rYFVaVp63YMuPLG/Y7VrUtbLup/DMt+XsuXrzoV7zH/ncm5p3fl8INboKr8vn4zI/65tzn846x7qF2zGlWrBDijx+H0v/pJfvxt/y/Ap7XvwGHHnsSTt15JUnIyzVo7n9sPd466mcWLnH/vM07vwZX/dw2Tnx9PTk4u1w5zuoN27NSZW++4O6rlCAQCjL79LoYNvYKCgnwGDhpMu3btoxqzNBUhcYVLVEMuK7h/J3cWjpgAVAV+Ay5T1T9LO35Xbug1DqNlT15YvV6i4sOfYnOhGOCSy8bFLLatyRAb1QL7PxDhpnd+Cvvv9OEzDo5pOgw1H1wKcBvQDlgK3K+q2yI5uaouAbruVwmNMRVKPNXgQl2Dm4xzY+AJoDbwuC8lMsZUaPHUTSTUNbgmqlrYjnhfRBb7USBjTMUWqAiZK0yhEpyISH32Th6QHPxcVaPTm9UYU6HFUX4LmeDq4qxgH/xxCmtxitOR1xhTyVSEIVjhCjVleWsfy2GMiRNxlN/C6gdnjDFF4ukuqiU4Y0xEKsJEluGyBGeMiUgc5beQHX1Djhi3u6jGVE4SR6syhKrBLcK5W1rSp4nKXdRYXbysViV2w2fO6NgsZrF/m/dIzGJv350Xs9iBGFZBojgy0jcJUYNTVW+mpDDGJJR4SnDhrKolInKxiNzpPm8pIkdHv2jGmIooYSa8dD0FHAcUzia4HXgyaiUyxlRoyUnhb7EWThGOUdXhwG4Ad7qjqlEtlTGmwvJy0RkRmSQiWSKSHrSvgYh8KCK/uD/ru/tFRB4XkRUislREypxAN5wElysiyexdVasx7hKCxpjKx+MZfV8AehfbNwqYq6rtgbnsXeqgD9De3YYCT5dZ1jAK8DgwA0gRkbHAZ0DsZko0xsSUl9Mlqep8oHiXszOBF93HLwIDg/ZPVseXQD0RaRrq/GV29FXVl0VkEc6U4wIMVFXP1zc1xsSHpAj6wYnIUJzaVqHxqlrW8nmpqroeQFXXu5PvAjQH1gQdl+HuK3VhkHA7+mYBU4Nfs46+xlROkdwcdZOZV+uBltYICU61AAATOUlEQVQnt1ThdvRtCfzpPq4HrAasn5wxlZAPHaUzRaSpW3trilPBAqfGlhZ0XAtgXagTlXoNTlXbqGpb4H3gDFVtpKoNgf7Am/tVfGNM3PJhyvKZwKXu40uBt4P2X+LeTT0W2FrYlC1NODcZjlLV2YVPVPU9nJXujTGVkMfdRKYCXwAHi0iGiAwB7gdOFZFfgFPd5wCzcVbnWwE8B1xd1vnDmU3kDxG5A5iC02S9GNgUxvt8s+DT+Txw/1gK8gsYNPgchlw5tOw3WeyIZGVuYNzdt7F50x8kSRL9B53N2edfzNOPP8znn86jSpUqNGuexq133Uft2iGXzI1Yzp49jBoxhNzcHPLz8zn+5FO46PJhfLfoKyY99RiqBVSrXoPrR99DsxYtPY0dbNWqldx2y941Z9dmrOGqq0dw4cWXhniXN8bcMZr58+fRoEFDpr/1btTjheLlAAVVvaCUl3qVcKwCwyM5f5nroro3G8YA3d1d84F7yrrJICIHA68F7WoL3KWqj5X2nt15ka+Lmp+fz4B+p/Psc8+TmprKheedzf0PPsKB7dpFeqqIxXvsP3fmhH3spj82sumPjRx0SAeyd+5k6CXn8c8H/8PGrEz+1vVoAoEAzz7hDN6/akTZC09HMtheVdm9axfVa9QgLy+XW4dfzpXXjuTRsXdyx7hHSWvdllkzpvHz8nRuuO3eMs/XtG61sGOXJj8/n76nnswLU16labPmYb8vUM7u/YsWfkONGjW447Zb9yvBVa+y/1OBvPDN6rD/Tv9xVMuYjtcq89tW1c2qeh1Os/REVb0unDuoqvqTqnZR1S7AkUA2Tn86T6UvW0paWitapKVRpWpVevftx7yP53odptLHbtioMQcd0gGAGjVr0qpNG/7YmMlRx3YjEHAaAh06dmZjlvcLWYsI1WvUACAvL4+8vLyisY7Z2TsByN65nYaNGnseuzTffPUlzdPSIkpu++PIrkdRp25dX2KVxcsmarSV2UQVkU44a6Q2cJ//AVyqqukh3/hXvYBfVfX3cpUyhKzMTJo0bVL0PCU1lWVLl3odxmIHWb9uLb/89COHHnb4X/bPfmcGPU49PSox8/PzueHKC1m/dg39Bp7HwR06MeKWu7jnlhFUPeAAatSoyUPPTI5K7JK8P2c2p/fu51u8iqQiJK5whVNffha4UVVbqWor4CYi79dyPkH96IKJyFARWSgiCyc+F3l3GS2hVevXLAaVMXZ2djZjRt3ANTfeSs1atYr2vzRpPMnJyZzau39U4iYnJ/P4pNd4/o33+fnHdH7/bQVvT3uZMf9+ghemv88pfc9kwn8fjkrs4nJzc5j/yUecclp0knlFJxFssRbOTYaaqvpx4RNVnSciNcMNICJVgQHA6JJeD+4IWJ5rcKmpTdiwfkPR86zMTFJSUkK8wzuVLXZeXi5jbr2BU07vR/cepxTtn/Pu23zx2Sc88tSEqCfZWrVr06lLVxZ9tYCVv/7MwR06AXBCz9O4++aIrj+X24LPPuWQQzrQsGEjX+JVNHFUgQurBvebiNwpIq3d7Q5gZQQx+gCLVdX7izPAYR07sXr1KjIy1pCbk8Oc2bM4qUfPaISq1LFVlX/fN4aWbdpy7kV77xp+9cVnTH1pEuMefoJq1apHJfbWLZvZsX07AHv27GbJoq9o0aoNO3fuYO0a56rHkm++pEUrf/qev//eLE7vUzmbpxBf88GFU4O7HLgHp3Ov4NxFvSyCGBdQSvPUC4FAgNG338WwoVdQUJDPwEGDadeufbTCVdrYy777lg/ee4e27doz5KKzAbjy6mt5/OH7yc3J4aZrnC4qHToezk2j7/I09uZNf/DYuLsoyC+gQAs4ocepHN2tOyNG3sm/7rgZSRJq1a7DdaPu9jRuSXbv2sXXX37O7XfeE/VYwUaNvJGF33zNli1/clqv7gy7egSDBp/jaxkKVYBp3sJWZjeR/Tq5SA2cwbFtVXVrWceXp4lqyi+SbiJei+WaDF50Eymv8nYT8YoX3UReX7Iu7L/Tc7o0i2k1LtRg+5mh3qiqA8o6uapmAw3LUS5jTAVVEZqe4QrVRD0Op/Y1FfiKinFTxBgTY/HURA2V4JrgjAO7AGc9hlnAVFX93o+CGWMqpniqwYWaTSRfVeeo6qXAsTgDXOeJyAjfSmeMqXASph+ciBwA9MOpxbXGmb7cpkoyphJLjqMaXKibDC8CHYH3cAbXRzI0yxiToOIov4Wswf0d2AkcBFwb1O4WnJlLvJ0TxxgTF6RCND7DU2qCU9V4ullijPFJotTgjDFmH5GsqhVrluCMMRGxGpyJC/VrVo1Z7DrVq8QsdkEUhyeWJZ6SQ2niaT44S3DGmIhEf9VA71iCM8ZEJCHuohpjTEniqIVqCc4YExmrwRljEpZdgzPGJCy7i2qMSVjxk94swRljImQ1OGNMwoqf9GYJzhgTqTjKcJbgjDERiacmakJMibTg0/kM6Hc6/XufysTnxlvsBI8NkJ+fzwXnDOLa4Vf5Gnf7tm3ccuN1DB7Ql7PP7MfS7771LXasv/NC8TRleVQTnIjcICLfi0i6iEwVEc8XpMzPz2fc2Ht56pkJzJg5izmz3+XXFSu8DmOxK0jsQlOnTKZNm7a+xgR46IFxdDv+BKbPnM3UN2bQps2BvsStCN95kTjKcFFLcCLSHLgW6KqqHYFk4Hyv46QvW0paWitapKVRpWpVevftx7yP53odxmJXkNgAmRs28OmnnzDQ55Xdd+zYwbeLFnLmWWcDUKVKVWrX8Wdi61h/58Ekgv9iLdpN1ABQXUQCQA1gndcBsjIzadK0SdHzlNRUMjMzvQ5jsStIbICH/j2O6264mSSfu9SvzVhDvQYNuOfO27jw3LO4b8wd7MrO9iV2rL/zYCLhb7EWtQSnqmuBh4DVwHpgq6p+UPw4ERkqIgtFZGF5riso+87t5de6jRbb/9jzP/mYBg0a0uGwjr7EC5afn89Py3/g7HPP55Vpb1K9eg1emPScL7Fj+Z3vEzeCrcxziawSkWUiskREFrr7GojIhyLyi/uzfnnLGs0man3gTKAN0AyoKSIXFz9OVceraldV7TrkyqERx0lNbcKG9RuKnmdlZpKSklL+glvsCh37u28X88nHH9Hv9J6MHnkTC7/+ittHjfQldkpqKimpqXQ8vDMAvU49jR+X/+BL7Fh+58WJSNhbmHqoahdV7eo+HwXMVdX2wFz3eblEs4l6CrBSVTeqai7OeqrdvA5yWMdOrF69ioyMNeTm5DBn9ixO6tHT6zAWu4LEHnH9TcyZ+wmz3v+Ifz34MF2PPoax9z/oS+xGjRqTmtqUVStXAvD1V1/Stm07X2LH8jsvzocm6pnAi+7jF4GB5T1RNPvBrQaOFZEawC6gF7DQ6yCBQIDRt9/FsKFXUFCQz8BBg2nXrr3XYSx2BYkdayNH386do0eSm5tL8xZpjLlvrC9xK9J3HkneEpGhQHDTbLyqBl+LUuADEVHgWfe1VFVdD6Cq60Wk3FVV0SjOTy8i9wDnAXnAt8AVqrqntON355VwocEkpPyC2P1Tx3JNhirJse16Wi2w/7c2v1uzPewvsHNa7ZDxRKSZqq5zk9iHwAhgpqrWCzrmT1Ut13W4qI5kUNUxwJhoxjDG+MvL7h+qus79mSUiM4CjgUwRaerW3poCWeU9f0KMZDDG+Mera3AiUlNEahc+Bk4D0oGZwKXuYZcCb5e3rDYW1RgTEQ97p6QCM9y7rQHgFVWdIyLfANNEZAjOtfxy9+i2BGeMiYhXTVRV/Q3oXML+TTg3JfebJThjTEQqwgiFcFmCM8ZEJI7ymyU4Y0yE4ijDWYIzxkQknia8tARnjIlI/KQ3S3DGmEjFUYazBGdiIjmGy6Mnx9NfaAVUESayDJclOGNMROLoEpwlOGNMZOIov1mCM8ZEJlYzCZeHJThjTETiKL9ZgjPGRCaO8pslOGNMhOIow1mCM8ZExLqJGGMSll2DM8YkrBj20Y6YJThjTITiJ8NZgjPGRCSemqgJsejMgk/nM6Df6fTvfSoTnxtf9hsstsW22OUmEWwxp6pR24DrcFbJ+R64vqzjd+WqRrrt2J2nPXv10l9+W63bdu7R/v3P0PTlv0R8HottsStDbC/+rtdt2aPhbtHML+FsUavBiUhH4EqcdQ47A/1FxPOluNOXLSUtrRUt0tKoUrUqvfv2Y97Hc70OY7EtdqWNXZyIhL3FWjSbqIcCX6pqtqrmAZ8Ag7wOkpWZSZOmTYqep6SmkpmZ6XUYi22xK23s4uKpiRrNBJcOdBeRhiJSA+gLpBU/SESGishCEVlYnusKiu6zz6//c1hsi10ZYu8b15uFn/0QtbuoqrpcRB4APgR2AN8BeSUcNx4YD7A7r4R/xTKkpjZhw/oNRc+zMjNJSUkpb7EttsW22GWIp5EMUb2LqqoTVfUIVe0ObAZ+8TrGYR07sXr1KjIy1pCbk8Oc2bM4qUdPr8NYbItdaWPvI47aqFHtByciKaqaJSItgbOA47yOEQgEGH37XQwbegUFBfkMHDSYdu08v5dhsS12pY1dXAXIW2ETtztHdE4u8inQEMgFblTVkLd9ytNENcaEr1pg//PT5p35Yf+dNqiZHNN8GNUanKqeGM3zG2P8VxFuHoQrIUYyGGNMSWwsqjEmIvFUg7MEZ4yJSDx1E7EEZ4yJiNXgjDEJyxKcMSZhWRPVGJOw4qkGZ91EjDER8XKkloj0FpGfRGSFiIzyuqyW4IwxkfEow4lIMvAk0AfoAFwgIh28LKo1UY0xEUnyro16NLBCVX8DEJFXgTOBH7wKUKES3P6MkxORoe7US76z2Ba7MsQuFMnfqYgMBYYG7RofVP7mwJqg1zKAY/a/hHslUhN1aNmHWGyLbbH9pKrjVbVr0BacnEtKlJ5OuJFICc4YE18y+Oss3y2AdV4GsARnjImVb4D2ItJGRKoC5wMzvQxQoa7B7adYXpew2Ba7MsT2lKrmicg1wPtAMjBJVb/3MkZUJ7w0xphYsiaqMSZhWYIzxiSsuE9wIlJNRL4Wke9E5HsRucfn+Mki8q2IvOtnXDf2KhFZJiJLRGShz7HricgbIvKjiCwXEc8XFCol7sHu5y3ctonI9X7EduPf4P6epYvIVBGp5mPs69y43/v5meNZ3F+DE2f125qqukNEqgCfAdep6pc+xb8R6ArUUdX+fsQMir0K6Kqqf/gZ1439IvCpqk5w74DVUNUtPpchGVgLHKOqv/sQrznO71cHVd0lItOA2ar6gg+xOwKv4vT+zwHmAMNU1fOlOBNJ3Nfg1LHDfVrF3XzJ2iLSAugHTPAjXkUhInWA7sBEAFXN8Tu5uXoBv/qR3IIEgOoiEgBq4HG/rRAOBb5U1WxVzQM+AQb5FDtuxX2Cg6Jm4hIgC/hQVb/yKfRjwC1AgU/xilPgAxFZ5A6J8UtbYCPwvNs8nyAiNX2MX+h8YKpfwVR1LfAQsBpYD2xV1Q98Cp8OdBeRhiJSA+jLXzvJmhIkRIJT1XxV7YLTE/potzofVSLSH8hS1UXRjhXC8ap6BM5sDMNFpLtPcQPAEcDTqvo3YCfg+VQ3objN4gHA6z7GrI8zGLwN0AyoKSIX+xFbVZcDDwAf4jRPvwPy/IgdzxIiwRVym0nzgN4+hDseGOBeB3sV6CkiU3yIW0RV17k/s4AZONdn/JABZATVlN/ASXh+6gMsVtVMH2OeAqxU1Y2qmgu8CXTzK7iqTlTVI1S1O7AZsOtvZYj7BCcijUWknvu4Os4v4Y/Rjquqo1W1haq2xmkqfaSqvvzfHEBEaopI7cLHwGk4zZioU9UNwBoROdjd1QsPp7gJ0wX42Dx1rQaOFZEa7s2tXsByv4KLSIr7syVwFv5//riTCEO1mgIvunfUkoBpqup7l40YSAVmOH9nBIBXVHWOj/FHAC+7TcXfgMv8CuxegzoVuMqvmACq+pWIvAEsxmkefou/Q6emi0hDIBcYrqp/+hg7LsV9NxFjjClN3DdRjTGmNJbgjDEJyxKcMSZhWYIzxiQsS3DGmIRlCc4AICKDRERF5JAyjvuHiDTbjzgnx2LmFVM5WYIzhS7AmSnj/DKO+wfOMCVjKjxLcAYRqYUz9GwIQQlORG5x55v7TkTuF5GzcaaGetmdi626OyddI/f4riIyz318tIh87g7G/zxo1IMxvkmEkQxm/w0E5qjqzyKyWUSOwBkpMRBnrrVsEWmgqpvdRUJuVtWFAFL6Kuc/At3dhUVOAcYBg6P/UYzZyxKcAad5+pj7+FX3eRLwvKpmA6jq5gjPWRdnCF17nGmdqnhUVmPCZgmuknPHNvYEOoqI4izfpsB0wps4NI+9lzqCp+++D/hYVQeJSGucWV6M8ZVdgzNnA5NVtZWqtlbVNGAlznQ8l7sD2xGRBu7x24HaQe9fBRzpPg5ugtbFmU4cnBsTxvjOEpy5AGcuuWDTce6UzgQWurMl3+y+9gLwTOFNBuAe4D8i8imQH3SOfwP/EpEFOLVCY3xns4kYYxKW1eCMMQnLEpwxJmFZgjPGJCxLcMaYhGUJzhiTsCzBGWMSliU4Y0zC+n+1AKp0nTfVJAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from ml_utils.classification import confusion_matrix_visual\n",
    "\n",
    "confusion_matrix_visual(y_test, preds, np.sort(white_wine.quality.unique()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([<matplotlib.axes._subplots.AxesSubplot object at 0x001E42F0>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x00651470>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x00669530>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x009C5070>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x009DE130>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x009F81F0>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x00A152B0>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x00A30970>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x00A4A450>],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 7 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from ml_utils.classification import plot_multi_class_pr_curve\n",
    "plot_multi_class_pr_curve(y_test, grid.predict_proba(X_test))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
